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Programmable Photonic Unitary Processor Enables Parametrized Differentiable Long-Haul Spatial Division Multiplexed Transmission

arXiv 2505.17381 ↗First posted 23 May 2025physics.optics cs.LG physics.app-phcited by 0

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Abstract (arXiv)

The explosive growth of global data traffic demands scalable and energy-efficient optical communication systems. Spatial division multiplexing (SDM) using multicore or multimode fibers is a promising solution to overcome the capacity limit of single-mode fibers. However, long-haul SDM transmission faces significant challenges due to modal dispersion, which imposes heavy computational loads on digital signal processing (DSP) for signal equalization. Here, we propose parameterized SDM transmission, where programmable photonic unitary processors are installed at intermediate nodes. Instead of relying on conventional digital equalization only on the receiver side, our approach enables direct optimization of the SDM transmission channel itself by the programmable unitary processor, which reduces digital post-processing loads. We introduce a gradient-based optimization algorithm using a differentiable SDM transmission model to determine the optimal unitary transformation. As a key enabler, we first implemented telecom-grade programmable photonic unitary processor, achieving a low-loss (2.1 dB fiber-to-fiber), wideband (full C-band), polarization-independent, and high-fidelity (R2>96% across the C-band) operation. We experimentally demonstrate 1300-km transmission using a three-mode fiber, achieving strong agreement between simulation and experiment. The optimized photonic processor significantly reduces modal dispersion and post-processing complexity. Our results establish a scalable framework for integrating photonic computation into the optical layer, enabling more efficient, high-capacity optical networks.

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References · 6

  1. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
  2. 2022The Forward-Forward Algorithm: Some Preliminary Investigationscited by 266
  3. 2020Photonics for artificial intelligence and neuromorphic computingcited by 112
  4. 2016Tunable Efficient Unitary Neural Networks (EUNN) and their application to RNNscited by 65
  5. 2019Intelligent Reflecting Surface-Assisted Millimeter Wave Communications: Joint Active and Passive Precoding Designcited by 8
  6. 2022Broadband physical layer cognitive radio with an integrated photonic processor for blind source separationcited by 1

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